{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "transform=transforms.Compose(\n",
    "    [transforms.ToTensor(),\n",
    "     transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainset=torchvision.datasets.CIFAR10(root='./data',train=True,download=False,transform=transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainloader=torch.utils.data.DataLoader(trainset,batch_size=4,shuffle=True,num_workers=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "testset=torchvision.datasets.CIFAR10(root='./data',train=False,download=False,transform=transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "testloader=torch.utils.data.DataLoader(testset,batch_size=4,shuffle=True,num_workers=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "classes=('plane','car','bird','cat','deer','dog','frog','horse','ship','truck')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def imshow(img):\n",
    "    img=img/2+0.5\n",
    "    npimg=img.numpy()\n",
    "    plt.imshow(np.transpose(npimg,(1,2,0)))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataiter=iter(trainloader)\n",
    "images,labels=dataiter.next()\n",
    "\n",
    "imshow(torchvision.utils.make_grid(images))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "images,labels=dataiter.next()\n",
    "\n",
    "imshow(torchvision.utils.make_grid(images))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "device=torch.device('cuda:0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CNNNet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(CNNNet,self).__init__()\n",
    "        self.conv1=nn.Conv2d(in_channels=3,out_channels=16,kernel_size=5,stride=1)\n",
    "        self.pool1=nn.MaxPool2d(kernel_size=2,stride=2)\n",
    "        self.conv2=nn.Conv2d(in_channels=16,out_channels=36,kernel_size=3,stride=1)\n",
    "        self.pool2=nn.MaxPool2d(kernel_size=2,stride=2)\n",
    "        self.fc1=nn.Linear(1296,128)\n",
    "        self.fc2=nn.Linear(128,10)\n",
    "        \n",
    "    def forward(self,x):\n",
    "        x=self.pool1(F.relu(self.conv1(x)))\n",
    "        x=self.pool2(F.relu(self.conv2(x)))\n",
    "        x=x.view(-1,36*6*6)\n",
    "        x=F.relu(self.fc2(F.relu(self.fc1(x))))\n",
    "        return x\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "net=CNNNet()\n",
    "net=net.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CNNNet(\n",
       "  (conv1): Conv2d(3, 16, kernel_size=(5, 5), stride=(1, 1))\n",
       "  (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  (conv2): Conv2d(16, 36, kernel_size=(3, 3), stride=(1, 1))\n",
       "  (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  (fc1): Linear(in_features=1296, out_features=128, bias=True)\n",
       "  (fc2): Linear(in_features=128, out_features=10, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "LR=0.001\n",
    "\n",
    "criterion=nn.CrossEntropyLoss()\n",
    "opm=optim.SGD(net.parameters(),lr=LR,momentum=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2000] loss:2.102\n",
      "[1, 4000] loss:1.751\n",
      "[1, 6000] loss:1.550\n",
      "[1, 8000] loss:1.440\n",
      "[1,10000] loss:1.361\n",
      "[1,12000] loss:1.302\n",
      "[2, 2000] loss:1.187\n",
      "[2, 4000] loss:1.163\n",
      "[2, 6000] loss:1.131\n",
      "[2, 8000] loss:1.110\n",
      "[2,10000] loss:1.053\n",
      "[2,12000] loss:1.053\n",
      "[3, 2000] loss:0.924\n",
      "[3, 4000] loss:0.931\n",
      "[3, 6000] loss:0.942\n",
      "[3, 8000] loss:0.909\n",
      "[3,10000] loss:0.914\n",
      "[3,12000] loss:0.921\n",
      "[4, 2000] loss:0.772\n",
      "[4, 4000] loss:0.782\n",
      "[4, 6000] loss:0.796\n",
      "[4, 8000] loss:0.798\n",
      "[4,10000] loss:0.819\n",
      "[4,12000] loss:0.813\n",
      "[5, 2000] loss:0.672\n",
      "[5, 4000] loss:0.704\n",
      "[5, 6000] loss:0.702\n",
      "[5, 8000] loss:0.710\n",
      "[5,10000] loss:0.711\n",
      "[5,12000] loss:0.710\n",
      "[6, 2000] loss:0.572\n",
      "[6, 4000] loss:0.600\n",
      "[6, 6000] loss:0.613\n",
      "[6, 8000] loss:0.636\n",
      "[6,10000] loss:0.641\n",
      "[6,12000] loss:0.684\n",
      "[7, 2000] loss:0.483\n",
      "[7, 4000] loss:0.499\n",
      "[7, 6000] loss:0.531\n",
      "[7, 8000] loss:0.581\n",
      "[7,10000] loss:0.586\n",
      "[7,12000] loss:0.570\n",
      "[8, 2000] loss:0.415\n",
      "[8, 4000] loss:0.431\n",
      "[8, 6000] loss:0.463\n",
      "[8, 8000] loss:0.508\n",
      "[8,10000] loss:0.521\n",
      "[8,12000] loss:0.532\n",
      "[9, 2000] loss:0.377\n",
      "[9, 4000] loss:0.397\n",
      "[9, 6000] loss:0.424\n",
      "[9, 8000] loss:0.432\n",
      "[9,10000] loss:0.458\n",
      "[9,12000] loss:0.483\n",
      "[10, 2000] loss:0.310\n",
      "[10, 4000] loss:0.352\n",
      "[10, 6000] loss:0.354\n",
      "[10, 8000] loss:0.394\n",
      "[10,10000] loss:0.416\n",
      "[10,12000] loss:0.425\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(10):\n",
    "    running_loss=0.0\n",
    "    for i,data in enumerate(trainloader,0):\n",
    "        inputs,labels=data\n",
    "        inputs,labels=inputs.to(device),labels.to(device)\n",
    "        \n",
    "        opm.zero_grad()\n",
    "        \n",
    "        outputs=net(inputs)\n",
    "        loss=criterion(outputs,labels)\n",
    "        loss.backward()\n",
    "        opm.step()\n",
    "        \n",
    "        running_loss+=loss.item()\n",
    "        if i%2000==1999:\n",
    "            print('[%d,%5d] loss:%.3f'% (epoch+1,i+1,running_loss/2000))\n",
    "            running_loss=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accurancy in 10000 iamges is : 67 %\n"
     ]
    }
   ],
   "source": [
    "correct=0\n",
    "total=0\n",
    "with torch.no_grad():\n",
    "    for data in testloader:\n",
    "        images,labels=data\n",
    "        images,labels=images.to(device),labels.to(device)\n",
    "        outputs=net(images)\n",
    "        _,predicted=torch.max(outputs.data,1)\n",
    "        total+=labels.size(0)\n",
    "        correct+=(predicted==labels).sum().item()\n",
    "print('Accurancy in 10000 iamges is : %d %%' %(100*correct/total))\n",
    "        \n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc of plane : 67 %\n",
      "Acc of   car : 72 %\n",
      "Acc of  bird : 69 %\n",
      "Acc of   cat : 50 %\n",
      "Acc of  deer : 70 %\n",
      "Acc of   dog : 44 %\n",
      "Acc of  frog : 72 %\n",
      "Acc of horse : 67 %\n",
      "Acc of  ship : 81 %\n",
      "Acc of truck : 76 %\n"
     ]
    }
   ],
   "source": [
    "class_correct=list(0. for i in range(10))\n",
    "class_total=list(0. for i in range(10))\n",
    "with torch.no_grad():\n",
    "    for data in testloader:\n",
    "        images,labels=data\n",
    "        images,labels=images.to(device),labels.to(device)\n",
    "        outputs=net(images)\n",
    "        _,predicted=torch.max(outputs.data,1)\n",
    "        c=(predicted==labels).squeeze()\n",
    "        for i in range(4):\n",
    "            label=labels[i]\n",
    "            class_correct[label]+=c[i].item()\n",
    "            class_total[label]+=1\n",
    "for i in range(10):\n",
    "    print('Acc of %5s : %2d %%'% (classes[i],100*class_correct[i]/class_total[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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